The process of selecting a path in a network along which to send network traffic is generally referred to as routing. The overall traffic performance of the network depends heavily on the selected path. In packet communication networks, routing directs packet from their source to their ultimate destination through intermediate nodes. These nodes are typically hardware devices such as routers, bridges, gateways, etc.
The path to be selected comprises several links between nodes of the network. The link through which to send traffic is chosen according to a metric. Thus, for network performance the chosen metric is of great importance.
In the art several link metrics to be used for link selection are known, e.g. an Expected Transmission Count ETX derived from a measured packet delivery ratio of a link and assuming that an acknowledgment for each transmitted packet is received in order to confirm its delivery. Additionally, link metric measurements are updated by simple averaging, e.g. using an ad-hoc first order smoothing filters. The routing process then uses the latest available link metric measurement. This approach does not take into account any known variation of a link transmission rate or transmission success rate and, thus, fails to deliver routing performance needed in state of the art smart grid communication networks with time-varying links.
However, stochastic learning is known in the art for routing time-varying links. This approach provides a simple linear reward-penalty learning algorithm which updates the probability of choosing a link depending on acknowledgments received. However, a reaction speed and a minimal probability for each link are the only parameters of this scheme and do not provide sufficient variability for state of the art smart grid systems.
In addition, for some applications, several link layer technologies may be deployed in a given network, i.e. there may be a copper, fiber-optic, wireless and a powerline link between nodes of the network. This plurality of link layer technologies is not taken into account by any routing protocol known in the art.
The paper by Tian Hui et al. entitled “Adaptive routing considering the state of receiver for Ad Hoc Networks” (12TH IEEE INTERNATIONAL CONFERENCE ON ELECTRONICS, CIRCUITS AND SYSTEMS, ICECS 2005) is concerned with adaptive routing in Mobile Ad Hoc Networks with mobile wireless nodes. The paper proposes a channel adaptive shortest routing that takes into account a packet queuing delay at the nodes. All communication links between any two neighboring nodes are wireless and modeled as Markov Channels with eight states when evaluating the proposed routing algorithm.